PREDICTION OF VEHICLE FLOW USING DECISION TREE
Keywords:
traffic flow, congestion, prediction, algorithm, model, decision tree, machine learning models, coefficient of determination, entropy.Abstract
This paper explores the traffic flow at the intersection of the ring road of Tashkent
city with Bogishamol Street. The study focuses on the movement of traffic and its dynamic indicators,
such as intensity, density, and speed, which were studied and reprocessed. The main problem
addressed in the research was forecasting traffic flow using decision trees, and based on this solution,
issues related to traffic management were considered. Alongside this, an analysis of factors affecting
traffic flow was conducted, and suggestions for their reduction were proposed. The analysis revealed
that special attention is currently being paid to the development of areas such as machine learning,
neural networks, and intelligent transportation systems, which are actively being implemented in the
transportation sector. Within these areas, an analysis of algorithms, methods, and models of machine
learning was conducted. The analysis showed that models such as decision trees, random forests, and
gradient boosting are widely used for traffic flow prediction. In this work, a decision tree was also
used to develop a model for predicting traffic flow on Bogishamol Street in Tashkent city, which
showed good results. The coefficient of determination was used to evaluate this indicator, which
showed an accuracy of 92%. This indicates the good predictive value of this model.
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